"""
Implementation of gaussian filter algorithm
"""
from cv2 import imread, cvtColor, COLOR_BGR2GRAY, imshow, waitKey
from numpy import pi, mgrid, exp, square, zeros, ravel, dot, uint8


def gen_gaussian_kernel(k_size, sigma):
    center = k_size // 2
    x, y = mgrid[0-center:k_size-center, 0-center:k_size-center]
    g = 1/(2*pi*sigma) * exp(-(square(x) + square(y))/(2*square(sigma)))
    return g


def gaussian_filter(image, k_size, sigma):
    height, width = image.shape[0], image.shape[1]
    # dst image height and width
    dst_height = height-k_size+1
    dst_width = width-k_size+1

    # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
    image_array = zeros((dst_height*dst_width, k_size*k_size))
    row = 0
    for i in range(0, dst_height):
        for j in range(0, dst_width):
            window = ravel(image[i:i + k_size, j:j + k_size])
            image_array[row, :] = window
            row += 1

    #  turn the kernel into shape(k*k, 1)
    gaussian_kernel = gen_gaussian_kernel(k_size, sigma)
    filter_array = ravel(gaussian_kernel)

    # reshape and get the dst image
    dst = dot(image_array, filter_array).reshape(dst_height, dst_width).astype(uint8)

    return dst


if __name__ == '__main__':
    # read original image
    img = imread(r'../image_data/lena.jpg')
    # turn image in gray scale value
    gray = cvtColor(img, COLOR_BGR2GRAY)

    # get values with two different mask size
    gaussian3x3 = gaussian_filter(gray, 3, sigma=1)
    gaussian5x5 = gaussian_filter(gray, 5, sigma=0.8)

    # show result images
    imshow('gaussian filter with 3x3 mask', gaussian3x3)
    imshow('gaussian filter with 5x5 mask', gaussian5x5)
    waitKey()
